doi: 10.1161/CIRCGENETICS.109.848986
Editorials |
A Gene-Centric Approach to Elucidating Cardiovascular Risk
From the Department of Medicine, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Canada.
Correspondence to Ruth McPherson, Department of Medicine, Division of Cardiology, University of Ottawa Heart Institute, 40 Ruskin Street H441, Ottawa, Ontario, Canada K1Y 4W7. E-mail: rmcpherson{at}ottawaheart.ca
Key Words: apolipoproteins • coronary disease • genetics • lipoproteins
Although behavioral precedents including diet and cigarette smoking play an important role, coronary artery disease (CAD) exhibits significant heritability,1 a part of which can be attributable to genetic variants affecting known biochemical and metabolic risk factors. In the last 2 years, impressive progress has been made in understanding the genetic basis of several of these using both candidate gene and genome-wide association study (GWAS) approaches.
Article see p 16
Candidate genes, coding for proteins of known biological significance in a disease process, provide a logical first step in understanding the genetics of common disease states. Populations of affected and unaffected individuals can be studied by genotyping common single-nucleotide polymorphisms (SNPs) within a gene and its regulatory sequences. Although economically attractive, this approach is acknowledged to have a number of limitations. By definition, studies are limited to genes with a known or suspected role in defining a given phenotype and do not provide new insight into biological pathways leading to disease. Furthermore, candidate gene associations often fail to replicate for multiple reasons. Sample sizes have often been inadequate to provide the statistical power required when multiple variants of small effect are tested. The original observation may have been false positive, emphasizing the need for stringent statistical thresholds. Other issues include heterogeneity of causality. For example, genetic variants affecting plasma lipid traits may have significant effects on CAD risk, but these may be less important than or interact with other risk factors including diabetes and smoking. Finally, appropriate measures must be taken to test for population stratification.
Genome wide association studies (GWASs) are is an alternative approach, facilitated by the International HapMap Project and advances in genotyping technology, permitting analysis of a million or more markers across the human genome. GWASs provides an unbiased approach that has led to the identification of novel loci associated with several complex phenotypes including CAD. Notably, many of these were unanticipated based on prior knowledge.2 GWASs have identified common variants linked to type 2 diabetes in genes affecting islet cell function, putting a renewed emphasis on β cell failure rather than adipocyte hypertrophy in the etiology of this disease.3 A number of known loci linked to lipoprotein metabolism have been identified by GWASs, supporting the overall validity of this approach. In addition, multiple novel genes or loci have been linked to plasma lipoprotein fractions4–11 (Table). One of the most robust associations with low-density lipoprotein cholesterol (LDL-C) and also linked to CAD risk is at the CELSR2-PSRC1-MYBPHL-SORT1 locus on 1p13. Although SORT1 mediates the endocytosis and degradation of lipoprotein lipase, an effect on LDL-C is unanticipated and CELSR2 and PSRC1 have no known effect on lipoprotein metabolism. A common nonsynonymous SNP in MLXIPL, a transcription factor involved in glucose utilization and energy metabolism, has shown a strong and consistent association with triglycerides providing new insights into its role in hepatic triglyceride synthesis. The 3 fatty acid desaturase genes encoding for proteins that function in arachidonate metabolism, FADS1, FADS2, and FADS3 on chromosomes 11q12, have been linked to triglycerides, LDL-C, and high-density lipoprotein cholesterol (HDL-C). GALNT2 at 1q42 coding for a receptor modifying glycosyltransferase has been consistently linked to HDL-C, pointing to a previously unknown pathway in HDL metabolism. Functional studies of these regions may provide new insight into lipid metabolism and reveal potential drug targets for CAD risk reduction.
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Beyond the identification of several novel loci, a few key messages have arisen from GWAS for plasma lipoprotein traits. Genetic variants predictive of LDL-C concentrations in genes of known function in apo B lipoprotein metabolism including APOB, LDLR, PCSK9, and HMGR are also predictive of CAD risk to an extent greater than would be anticipated by the apparent effect on LDL-C concentrations11 supporting the premise that genetic variants affecting LDL-C are a better marker of lifetime exposure to LDL-C as compared with a single measurement later in life. Despite the fact that HDL-C is a more powerful predictor of CAD risk than LDL-C, not all common variants associated with HDL-C or Apo AI have shown a consistent association with CAD. Multiple explanations are possible. The functional relationship of HDL to CAD risk is inherently complex and plasma concentrations of HDL-C are not always a reliable marker of reverse cholesterol transport. Genetic variation at the CETP locus has important reciprocal effects of plasma concentrations of CETP and HDL-C/Apo AI. Although CETP facilitates the generation of atherogenic lipoproteins, it also functions directly in reverse cholesterol transport and available data suggest that either high or low levels of CETP may increase atherosclerosis susceptibility. In contrast to CETP, SNPs in hepatic lipase (LIPC) and lipoprotein lipase have been associated with both HDL-C and CAD risk.5
It must be acknowledged that despite the recent successes of the GWAS approach, the ability to identify the genetic basis of complex disease remains modest. Despite the fact that heritability (h2) estimates for plasma lipoprotein fractions including triglycerides, LDL-C, and HDL-C ranges from 0.5 to 0.6,14 in large study populations, common variants in both known and novel gene regions have accounted for only 3% to 10% of variability in plasma lipoprotein fractions. Current GWAS technology is limited by reliance on HapMap, based on genetic data from a small number of individuals and providing proxies for the majority of SNPs with a minor allele frequency (MAF) >5%. The Encyclopedia of DNA Elements project indicated that 
60% of SNPs had an MAF <0.05. As noted by Gorlov et al,15 there is an inverse relationship between MAF and proportion of nsSNPs predicted to be damaging. Thus, multiple lower frequency variants are likely to have a cumulatively more important phenotypic effect. Indeed, current GWAS platforms fail to capture most of the rare variants present on the nsSNP platform.16 Multiple lower frequency common variants (MAF between 0.5% and 5%) have been shown to contribute importantly to complex phenotypes relevant to CAD. Resequencing of the PCSK9 gene identified low-frequency common variants (MAF, 0.5–1.0) affecting LDL-C concentrations and CAD risk.17 Even rarer variants with an MAF <0.5% are important determinants of extreme phenotypes. Approximately 15% of subjects with HDL-C below the 10% for age and sex harbor multiple rare mutations in ABCA1, APOA1, or PLTP.18 In addition to lack of rare variant coverage, current GWAS platforms still have significant gaps in coverage. For example, the APOE SNPs, rs7412 and rs429358, defining the common 
2, 3, and 4 isoforms are not genotyped on the Affymetrix or Illumina arrays, and the best proxy, rs4420638, lies 3' of APOC1 and is in only partial linkage dysequilibrium with the Apo E 4 SNP, rs429358 (r2=0.72).
Thus, considerable interest remains in the use of a candidate gene approach for the evaluation of cardiovascular risk. Keating et al19 have recently described the design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. Led by investigators at the Institute of Translational Medicine and Therapeutics (ITMAT), the Broad Institute and the Candidate-gene Association Resource (CARe) supported by the National Heart, Lung and Blood Institute, the ITMAT-Broad-CARe (IBC) array provides an important complement to current GWAS products. In addition to 1349 loci of potential involvement in phenotypes of interest, the array includes dense coverage of 435 genes and regions of high biological plausibility in relationship to cardiovascular disease or metabolic phenotypes. Tag SNPs were selected to capture variation with MAF >0.02 and r2>0.8 in HapMap populations and Seattle SNPs, and all nsSNPs were included. Notably, more than 20% of the IBC group I SNPs have not been assayed in HapMap and were derived from Seattle SNPs or published reports.
In this issue of Circulation: Cardiovascular Genetics, on a smaller scale but similarly based on previously published data on genetic variants associated with CAD risk factors or myocardial infarction (MI) or biological pathways associated with these risk factors, the INTERHEART investigators have genotyped 1326 SNPs from 103 genes in 8034 MI cases and controls from 5 ethnic groups, including 2306 South Asian subjects, an understudied population at apparently increased risk for CAD12 (Table). In accordance with other recent attempts to replicate candidate gene findings in CAD, none of these were associated with MI after adjustment for multiple testing. Of 940 SNPs in 69 genes tested for association with MI risk factors, 13 showed significant associations. Each of these have been previously well studied, including SNPs in APOE, LDLR, and APOB associated with Apo B concentrations and 3 tightly linked SNPs in CETP associated with Apo AI levels. By reanalyzing the association of these 13 SNPs with MI risk, the investigators corroborate previous reports on the relationship of SNPs defining the Apo E 2 (rs7412) and 4 (rs429358) isoforms to MI risk and the association of LDLR SNPs (rs6511720 and rs1433099) to both Apo B concentrations and MI risk. In accordance with other recent data, CETP SNPs associated with Apo AI concentrations were not associated with MI risk. Overall, the 13 significant SNPs accounted for less than 5% of variability in apolipoprotein concentrations. Because plasma lipids were not measured and samples were nonfasting, it was not possible to study the relationship of candidate gene SNPs to plasma triglycerides or other lipid traits.
Importantly, more attention must be given to phenotype ascertainment in both GWAS and candidate gene studies. Plasma lipoprotein levels vary over the course of a lifetime, and a single measurement20 is unlikely to reflect the lifetime exposure to a given lipid risk factor. Many CAD cases have been on long-term statin therapy obscuring the effect of specific polymorphisms on LDL-C. Accurate and precise phenotyping is similarly important for CAD. In many GWAS, CAD cases are compared with age- and sex-matched controls but given the prevalence of subclinical atherosclerosis in middle aged individuals, many 50-year-old controls may harbor a similar disease burden to cases with incident MI. Angiographic ascertainment of disease burden is an optimal solution but is not generally feasible in apparently healthy controls. An alternative and attractive approach is to compare young subjects with extensive multivessel CAD with healthy elderly controls although confounding is possible due to other genetic variants associated with longevity.
Overall, as indicated by Anand et al,12 a candidate gene approach is valuable and complementary to the discovery nature of GWAS. In 2008, several genome-wide and candidate gene studies provided new information on the genetic basis of variability in plasma lipoprotein concentrations and relationship to cardiovascular disease. It is notable that studies7,13 that have supplemented standard arrays with custom content have had the greatest success in explaining variation in individual lipoprotein traits (Table). Further success in deciphering the genetic components of lipoprotein metabolism and CAD risk will require access to large comprehensive primary data sets to dissect potentially important gene–gene and gene–environment interactions8 and dense resequencing of significant loci that are likely to harbor multiple rare variants of cumulative importance.
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Acknowledgments |
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Sources of Funding
The Canadian Institutes of Health Research and the Heart and Stroke Foundation of Ontario supported this study.
Disclosures
None.
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Footnotes |
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The opinions expressed in this editorial are not necessarily those of the editors or of the American Heart Association.
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References |
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 Top References |
|---|
1. Lusis AJ, Mar R, Pajukanta P. Genetics of atherosclerosis. Annu Rev Genomics Hum Genet. 2004; 5: 189–218.[CrossRef][Medline]
2. McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR, Hinds DA, Pennacchio LA, Tybjaerg-Hansen A, Folsom AR, Boerwinkle E, Hobbs HH, Cohen JC. A common allele on chromosome 9 associated with coronary heart disease. Science. 2007; 316: 1488–1491.
3. Frayling TM, McCarthy MI. Genetic studies of diabetes following the advent of the genome-wide association study: where do we go from here? Diabetologia. 2007; 50: 2229–2233.[CrossRef][Medline]
4. Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM, Pramstaller PP, Penninx BW, Janssens AC, Wilson JF, Spector T, Martin NG, Pedersen NL, Kyvik KO, Kaprio J, Hofman A, Freimer NB, Jarvelin MR, Gyllensten U, Campbell H, Rudan I, Johansson A, Marroni F, Hayward C, Vitart V, Jonasson I, Pattaro C, Wright A, Hastie N, Pichler I, Hicks AA, Falchi M, Willemsen G, Hottenga JJ, de Geus EJ, Montgomery GW, Whitfield J, Magnusson P, Saharinen J, Perola M, Silander K, Isaacs A, Sijbrands EJ, Uitterlinden AG, Witteman JC, Oostra BA, Elliott P, Ruokonen A, Sabatti C, Gieger C, Meitinger T, Kronenberg F, Doring A, Wichmann HE, Smit JH, McCarthy MI, van Duijn CM, Peltonen L. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet. 2009; 41: 47–55.[CrossRef][Medline]
5. Kathiresan S, Melander O, Anevski D, Guiducci C, Burtt NP, Roos C, Hirschhorn JN, Berglund G, Hedblad B, Groop L, Altshuler DM, Newton-Cheh C, Orho-Melander M. Polymorphisms associated with cholesterol and risk of cardiovascular events. N Engl J Med. 2008; 358: 1240–1249.
6. Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, Cooper GM, Roos C, Voight BF, Havulinna AS, Wahlstrand B, Hedner T, Corella D, Tai ES, Ordovas JM, Berglund G, Vartiainen E, Jousilahti P, Hedblad B, Taskinen MR, Newton-Cheh C, Salomaa V, Peltonen L, Groop L, Altshuler DM, Orho-Melander M. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet. 2008; 40: 189–197.[Medline]
7. Kathiresan S, Willer CJ, Peloso GM, Demissie S, Musunuru K, Schadt EE, Kaplan L, Bennett D, Li Y, Tanaka T, Voight BF, Bonnycastle LL, Jackson AU, Crawford G, Surti A, Guiducci C, Burtt NP, Parish S, Clarke R, Zelenika D, Kubalanza KA, Morken MA, Scott LJ, Stringham HM, Galan P, Swift AJ, Kuusisto J, Bergman RN, Sundvall J, Laakso M, Ferrucci L, Scheet P, Sanna S, Uda M, Yang Q, Lunetta KL, Dupuis J, de Bakker PI, O'Donnell CJ, Chambers JC, Kooner JS, Hercberg S, Meneton P, Lakatta EG, Scuteri A, Schlessinger D, Tuomilehto J, Collins FS, Groop L, Altshuler D, Collins R, Lathrop GM, Melander O, Salomaa V, Peltonen L, Orho-Melander M, Ordovas JM, Boehnke M, Abecasis GR, Mohlke KL, Cupples LA. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet. 2009; 41: 56–65.[CrossRef][Medline]
8. Sabatti C, Service SK, Hartikainen AL, Pouta A, Ripatti S, Brodsky J, Jones CG, Zaitlen NA, Varilo T, Kaakinen M, Sovio U, Ruokonen A, Laitinen J, Jakkula E, Coin L, Hoggart C, Collins A, Turunen H, Gabriel S, Elliot P, McCarthy MI, Daly MJ, Jarvelin MR, Freimer NB, Peltonen L. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet. 2009; 4: 35–46.
9. Sandhu MS, Waterworth DM, Debenham SL, Wheeler E, Papadakis K, Zhao JH, Song K, Yuan X, Johnson T, Ashford S, Inouye M, Luben R, Sims M, Hadley D, McArdle W, Barter P, Kesaniemi YA, Mahley RW, McPherson R, Grundy SM, Bingham SA, Khaw KT, Loos RJ, Waeber G, Barroso I, Strachan DP, Deloukas P, Vollenweider P, Wareham NJ, Mooser V. LDL-cholesterol concentrations: a genome-wide association study. Lancet. 2008; 371: 483–491.[CrossRef][Medline]
10. Wallace C, Newhouse SJ, Braund P, Zhang F, Tobin M, Falchi M, Ahmadi K, Dobson RJ, Marcano AC, Hajat C, Burton P, Deloukas P, Brown M, Connell JM, Dominiczak A, Lathrop GM, Webster J, Farrall M, Spector T, Samani NJ, Caulfield MJ, Munroe PB. Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia. Am J Hum Genet. 2008; 82: 139–149.[CrossRef][Medline]
11. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, Heath SC, Timpson NJ, Najjar SS, Stringham HM, Strait J, Duren WL, Maschio A, Busonero F, Mulas A, Albai G, Swift AJ, Morken MA, Narisu N, Bennett D, Parish S, Shen H, Galan P, Meneton P, Hercberg S, Zelenika D, Chen WM, Li Y, Scott LJ, Scheet PA, Sundvall J, Watanabe RM, Nagaraja R, Ebrahim S, Lawlor DA, Ben-Shlomo Y, vey-Smith G, Shuldiner AR, Collins R, Bergman RN, Uda M, Tuomilehto J, Cao A, Collins FS, Lakatta E, Lathrop GM, Boehnke M, Schlessinger D, Mohlke KL, Abecasis GR. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008; 40: 161–169.[Medline]
12. Anand SS, Xie C, Pare G, Montpetit A, Rangarajan S, McQueen MJ, Cordell HJ, Keavney B, Yusuf S, Hudson TJ, Engert JC. Genetic variants associated with myocardial infarction risk factors in over 8000 individuals from five ethnic groups: the INTERHEART genetics study. Circ Cardiovasc Genet. 2009; 2: 16–25.
13. Chasman DI, Pare G, Zee RY, Parker AN, Cook NR, Buring JE, Kwiatkowski DJ, Rose LM, Smith JD, Williams PT, Rieder MJ, Rotter JI, Nickerson DA, Krauss RM, Miletich JP, Ridker PM. Genetic loci associated with plasma concentration of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein A1 and apolipoprotein B among white women in genome-wide analysis with replication. Circ Cardiovasc Genet. 2008; 1: 21–30.
14. Yu Y, Wyszynski DF, Waterworth DM, Wilton SD, Barter PJ, Kesaniemi YA, Mahley RW, McPherson R, Waeber G, Bersot TP, Ma QL, Sharma SS, Montgomery DS, Middleton LT, Sundseth SS, Mooser V, Grundy SM, Farrer LA. Multiple QTLs influencing triglyceride and HDL and total cholesterol levels identified in families with atherogenic dyslipidemia. J Lipid Res. 2005; 46: 2202–2213.
15. Gorlov IP, Gorlova OY, Sunyaev SR, Spitz MR, Amos CI. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am J Hum Genet. 2008; 82: 100–112.[CrossRef][Medline]
16. Evans DM, Barrett JC, Cardon LR. To what extent do scans of non-synonymous SNPs complement denser genome-wide association studies? Eur J Hum Genet. 2008; 16: 718–723.[CrossRef][Medline]
17. Cohen JC, Boerwinkle E, Mosley TH, Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med. 2006; 354: 1264–1272.
18. Cohen JC, Kiss RS, Pertsemlidis A, Marcel YL, McPherson R, Hobbs HH. Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science. 2004; 305: 869–872.
19. Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, Glessner JT, Galver L, Barrett JC, Grant SF, Farlow DN, Chandrupatla HR, Hansen M, Ajmal S, Papanicolaou GJ, Guo Y, Li M, Derohannessian S, de Bakker PI, Bailey SD, Montpetit A, Edmondson AC, Taylor K, Gai X, Wang SS, Fornage M, Shaikh T, Groop L, Boehnke M, Hall AS, Hattersley AT, Frackelton E, Patterson N, Chiang CW, Kim CE, Fabsitz RR, Ouwehand W, Price AL, Munroe P, Caulfield M, Drake T, Boerwinkle E, Reich D, Whitehead AS, Cappola TP, Samani NJ, Lusis AJ, Schadt E, Wilson JG, Koenig W, McCarthy MI, Kathiresan S, Gabriel SB, Hakonarson H, Anand SS, Reilly M, Engert JC, Nickerson DA, Rader DJ, Hirschhorn JN, FitzGerald GA. Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PLoS ONE. 2008; 3: e3583.[CrossRef][Medline]
20. Marcovina SM, Gaur VP, Albers JJ Biological variability of cholesterol, triglyceride, low- and high-density lipoprotein cholesterol, lipoprotein (a), and apolipoproteins A-I and B. Clin Chem. 1994; 40: 574–578.
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